Don't expect an AI agent to invent a successful sales process. First, have your human team identify and document what works—effective emails, scripts, and objection handling. Then, train the AI on this proven playbook to execute it flawlessly and at scale. The AI is a scaling tool, not a strategist from day one.

Before delegating a complex task, use a simple prompt to have a context-aware system generate a more detailed and effective prompt. This "prompt-for-a-prompt" workflow adds necessary detail and structure, significantly improving the agent's success rate and saving rework.

Unlike older sales tools, AI agents shouldn't be handed to individual SDRs to manage. This approach leads to failure. Instead, centralize the strategy: a core team must own agent training, contact routing, and performance tuning to ensure a consistent and effective GTM motion across the entire organization.

A primary AI agent interacts with the customer. A secondary agent should then analyze the conversation transcripts to find patterns and uncover the true intent behind customer questions. This feedback loop provides deep insights that can be used to refine sales scripts, marketing messages, and the primary agent's programming.

Marketers mistakenly believe implementing AI means full automation. Instead, design "human-in-the-loop" workflows. Have an AI score a lead and draft an email, but then send that draft to a human for final approval via a Slack message with "approve/reject" buttons. This balances efficiency with critical human oversight.

Create a dedicated AI agent pre-loaded with your company's specific deal qualifiers (budget, timeline, ICP). Feed it discovery call notes, and it can instantly score the opportunity or flag it as disqualified, preventing reps from wasting time on deals that will never close.

Clogging a sales calendar with unqualified prospects is a major bottleneck. Deploy an AI voice agent to call new leads and ask a single, ruthless qualifying question. This immediately filters out bad fits, freeing up sales reps to focus only on high-probability deals.

Traditional pre-qualification uses rigid scripts, potentially missing high-value clients who don't fit the mold. Agentic AI analyzes conversation nuances to identify various customer archetypes and high-intent signals beyond the primary avatar, ensuring top prospects aren't overlooked.

Many companies fail with AI prospecting because their outputs are generic. The key to success isn't the AI tool but the quality of the data fed into it and relentless prompt iteration. It took the speakers six months—not six weeks—to outperform traditional methods, highlighting the need for patience and deep customization with sales team feedback.